RISS 학술연구정보서비스

검색

인기 검색어

    다국어 입력

    http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

    변환된 중국어를 복사하여 사용하시면 됩니다.

    예시)
    • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
    • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
    닫기
    KCI등재

    The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies

    한글로보기

    https://www.riss.kr/link?id=A103646969

    • 0

      상세조회
    • 0

      다운로드
    서지정보 열기
    • 내보내기
    • 내책장담기
    • 공유하기
    • 오류접수

    부가정보

    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology.
    Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they can quickly develop vaccine through big data or successfully use big data in predicting the situation in which the parts of their product may experience breakdown. However, the small and medium-sized manufacturing companies(SMMCs) are facing difficulties in realizing and predicting their need for utilizing manufacturing big data technology. It is difficult to implement technology of which the usage needs are unclear. For this reason, only 25% of firms are reported to utilize the big data technology.
    The purpose of this paper is to identify the underlying needs for manufacturing big data in the context of SMMCs and to assess the extent to which those needs are related to the intention to adopt the manufacturing big data technology. Additionally, this study is designed to identify which companies have higher level of usage needs of the manufacturing big data technology This study comprehensively reviewed previous literature and conducted in-depth interviews with three domestic manufacturing firms in order to understand potential usage needs of SMMCs for the manufacturing big data technology. This study developed the following three big dimensions of manufacturing big data usage need based on the comprehensive review of literature and interview - (1) traditional production needs(cost, quality, flexibility, delivery), (2) social responsibility needs(environmental protection and safety), and (3) customer service-oriented new business development needs. Also, this study suggested the following two driving factors positively influencing the needs - (1) competition and (2) information technology capabilities.
    This study developed a research model that presents two driving factors → three dimensions of usage needs of manufacturing big data → adoption intention of manufacturing big data technology. Further, this study conducted a survey with 200 SMMCs in order to empirically test the validity of the research model. The empirical test results were summarized as follows. First, the significant usage needs of small and medium-sized manufacturing companies for the manufacturing big data technology include (i) the need to improve production performances in terms of cost, quality, flexibility, and delivery, and (ii) the need to improve social responsibility performances in the areas of environmental protection and safety. These needs were found to be significantly correlated with the intention of SMMCs to adopt the manufacturing big data technology. However, SMMCs do not want to use manufacturing big data to develop a customer service-oriented new business model. Second, competition was correlated with the needs to utilize manufacturing big data to improve both production and social responsibility performances in a positive manner. Third, as in the case of competition, SMMCs with higher level of IT capabilities are likely to have stronger need to use manufacturing big data to increase their production and social responsibility performances.
    This study has following practical implications. First, the results of this study indicate that the intention of the SMMCs to adopt manufacturing big data technology are related to their needs to improve the performances in traditional production areas and social responsibility areas. These are the perceived purposes of the small and medium-sized companies to introduce the manufacturing big data technology. Therefore, Korean government research institutes or technical consulting firms have to develop appropriate manufacturing big data technologies to meet these purposes. For example, as small and medium-sized manufacturing companies do not introduce manufacturing big data technologies to develop a new business model that is customer service-oriented...
    번역하기

    Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology. Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they ...

    Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology.
    Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they can quickly develop vaccine through big data or successfully use big data in predicting the situation in which the parts of their product may experience breakdown. However, the small and medium-sized manufacturing companies(SMMCs) are facing difficulties in realizing and predicting their need for utilizing manufacturing big data technology. It is difficult to implement technology of which the usage needs are unclear. For this reason, only 25% of firms are reported to utilize the big data technology.
    The purpose of this paper is to identify the underlying needs for manufacturing big data in the context of SMMCs and to assess the extent to which those needs are related to the intention to adopt the manufacturing big data technology. Additionally, this study is designed to identify which companies have higher level of usage needs of the manufacturing big data technology This study comprehensively reviewed previous literature and conducted in-depth interviews with three domestic manufacturing firms in order to understand potential usage needs of SMMCs for the manufacturing big data technology. This study developed the following three big dimensions of manufacturing big data usage need based on the comprehensive review of literature and interview - (1) traditional production needs(cost, quality, flexibility, delivery), (2) social responsibility needs(environmental protection and safety), and (3) customer service-oriented new business development needs. Also, this study suggested the following two driving factors positively influencing the needs - (1) competition and (2) information technology capabilities.
    This study developed a research model that presents two driving factors → three dimensions of usage needs of manufacturing big data → adoption intention of manufacturing big data technology. Further, this study conducted a survey with 200 SMMCs in order to empirically test the validity of the research model. The empirical test results were summarized as follows. First, the significant usage needs of small and medium-sized manufacturing companies for the manufacturing big data technology include (i) the need to improve production performances in terms of cost, quality, flexibility, and delivery, and (ii) the need to improve social responsibility performances in the areas of environmental protection and safety. These needs were found to be significantly correlated with the intention of SMMCs to adopt the manufacturing big data technology. However, SMMCs do not want to use manufacturing big data to develop a customer service-oriented new business model. Second, competition was correlated with the needs to utilize manufacturing big data to improve both production and social responsibility performances in a positive manner. Third, as in the case of competition, SMMCs with higher level of IT capabilities are likely to have stronger need to use manufacturing big data to increase their production and social responsibility performances.
    This study has following practical implications. First, the results of this study indicate that the intention of the SMMCs to adopt manufacturing big data technology are related to their needs to improve the performances in traditional production areas and social responsibility areas. These are the perceived purposes of the small and medium-sized companies to introduce the manufacturing big data technology. Therefore, Korean government research institutes or technical consulting firms have to develop appropriate manufacturing big data technologies to meet these purposes. For example, as small and medium-sized manufacturing companies do not introduce manufacturing big data technologies to develop a new business model that is customer service-oriented...

    더보기

    참고문헌 (Reference)

    1 이종운, "클라우드 서비스 생태계 활성화 방안: 공급자와 사용자 관점 기반" 엘지씨엔에스 13 (13): 73-88, 2014

    2 노규성, "제조실행시스템에의 빅데이터 적용방안에 대한 탐색적 연구" 한국디지털정책학회 12 (12): 305-311, 2014

    3 곽교, "기업의 사회적 책임이 기업에 대한 신뢰 및 평판과 고객충성도에 미치는 영향: 소비자의 윤리적 의식의 조절효과를 중심으로" 한국기업경영학회 23 (23): 23-42, 2016

    4 김근아, "기업 내적 IT 자원이 기업 민첩성과 성과에 미치는 영향: 관리적 IT 능력과 경영진 존재의 조절효과" 한국경영정보학회 15 (15): 39-69, 2013

    5 김종영, "공급사슬 지향성, 민첩성 및 성과 간의 관계: 수도권 소재 중소 제조 기업을 대상으로" 한국기업경영학회 22 (22): 229-247, 2015

    6 김진호, "경영학 연구에서의 구조방정식 모형의 적용: 문헌 연구와 비판" 한국경영학회 36 (36): 897-923, 2007

    7 김지대, "가치혁신 생산전략의 성과: 생산전략의 새로운 패라다임" 한국기업경영학회 20 (20): 273-293, 2013

    8 Shih, W. C., "What it takes to reshore manufacturing successfully" 55-62, 2014

    9 Jang, Y. J., "Utilizing big data technology in manufacturing sector" 29 (29): 30-35, 2012

    10 Min, G., "Utilizing big data in the manufacturing industry" 719-736, 2013

    1 이종운, "클라우드 서비스 생태계 활성화 방안: 공급자와 사용자 관점 기반" 엘지씨엔에스 13 (13): 73-88, 2014

    2 노규성, "제조실행시스템에의 빅데이터 적용방안에 대한 탐색적 연구" 한국디지털정책학회 12 (12): 305-311, 2014

    3 곽교, "기업의 사회적 책임이 기업에 대한 신뢰 및 평판과 고객충성도에 미치는 영향: 소비자의 윤리적 의식의 조절효과를 중심으로" 한국기업경영학회 23 (23): 23-42, 2016

    4 김근아, "기업 내적 IT 자원이 기업 민첩성과 성과에 미치는 영향: 관리적 IT 능력과 경영진 존재의 조절효과" 한국경영정보학회 15 (15): 39-69, 2013

    5 김종영, "공급사슬 지향성, 민첩성 및 성과 간의 관계: 수도권 소재 중소 제조 기업을 대상으로" 한국기업경영학회 22 (22): 229-247, 2015

    6 김진호, "경영학 연구에서의 구조방정식 모형의 적용: 문헌 연구와 비판" 한국경영학회 36 (36): 897-923, 2007

    7 김지대, "가치혁신 생산전략의 성과: 생산전략의 새로운 패라다임" 한국기업경영학회 20 (20): 273-293, 2013

    8 Shih, W. C., "What it takes to reshore manufacturing successfully" 55-62, 2014

    9 Jang, Y. J., "Utilizing big data technology in manufacturing sector" 29 (29): 30-35, 2012

    10 Min, G., "Utilizing big data in the manufacturing industry" 719-736, 2013

    11 Lee, H. H., "Utilizing big data for reinforcing manufacturing firms’ competitiveness" 45-54, 2014

    12 Kim, S. B., "Utilizing big data for improving manufacturing process quality" 20 (20): 42-45, 2013

    13 Kim, J. D., "The utilization strategy of big data in the manufacturing strategy" Chungbuk National University 2014

    14 Shin, S., "The typology and status of Big data technology" National Information Society Agency, Big Data Strategy Center 2013

    15 Chi, S. Y., "The overview of predictive manufacturing system" Electronics and Telecommunications Research Institute 2014

    16 Cho, H. J., "The industry 4. 0 of German and its implication" Hyundai Research Institute 2013

    17 Hamel, G., "The future of management" Harvard Business School Press 2007

    18 Park, H., "The fourth industrial revolution and cyber-physical system" 52-59, 2014

    19 Shin, Y., "The evolving analytical tool" 88-91, 2012

    20 Cho, W. S., "The analytical tools for utilizing manufacturing big data" 2014

    21 Lee, J., "Recent advances and trends in predictive manufacturing systems in big data environment" 1 : 38-41, 2013

    22 Shin, S. -J., "Predictive analytics model for power consumption in manufacturing" 15 : 153-158, 2014

    23 Bagozzi, R. P., "On the evaluation of structural equation models" 16 (16): 74-94, 1988

    24 Jang, Y. J., "New paradigm as seen in Google’s unmanned automobile" 64-70, 2012

    25 Hair, J. F., "Multivariate data analysis with readings" Macmillan Publishing Company 2006

    26 Kim, K. Y., "Mastering the quality staircase, step by step" 17-21, 1997

    27 Dutta, D., "Managing a big project : The case of Ramco Cements Limited" 165 : 293-306, 2015

    28 Ferdows, K., "Lasting improvements in manufacturing performance : In search of a new theory" 9 : 168-183, 1990

    29 Mello, R., "Is big data the next big thing in performance measurement systems?" 1-10, 2014

    30 Lee, J., "Internet of things in the manufacturing industry" 60-65, 2014

    31 ITU, "Internet of things global standards initiative"

    32 Setlur, B., "Informed manufacturing: The next industrial revolution" 1-13, 2014

    33 McKinsey Global Institute, "Game changers: Five opportunities for US growth and renewal" Mckinsey & Company 2013

    34 Barney, J., "Firm resource and sustained competitive advantage" 17 (17): 99-120, 1991

    35 Vargo, S. L., "Evolving to a new dominant logic for marketing" 68 : 1-17, 2004

    36 Davenport, T. H., "Enterprise analytics: Optimize performance, process, and decisions through big data" FT Press 2013

    37 Iansiti, M., "Digital ubiquity : How connections, sensors, and data are revolutionizing business" 90-99, 2014

    38 Yoo, K. H., "Department of Software Seminar on Big Data" Chungbuk National University 2014

    39 Cebr, "Data equity: Unlocking the value of big data"

    40 Park, E. L., "Data based segmentation and summarization for sensor data in semiconductor manufacturing" 41 : 2619-2629, 2014

    41 Kim, G. H., "Customizing and analyzing big data" 72-78, 2012

    42 Lopez Research, "Building smarter manufacturing with the internet of things(IoT)"

    43 McKinsey Global Institute, "Big data: The next frontier for innovation, competition, and productivity" Mckinsey & Company 2011

    44 Choi, J. S., "Big data, 3D printer, internet of things" 80-86, 2014

    45 Etnews, "Big data solution development for government and small and medium-sized manufacturing firms"

    46 Frizzo-Barker, J., "An empirical study of the rise of big data in business scholarship" 36 : 403-413, 2016

    47 Lee, H. H., "An approach for utilizing big data for the competitiveness of manufacturing industry" Korea Institute for Industrial Economics & Trade 2013

    48 Park, J., "A study about modeling of big data system to improve the petrochemical processing" Dong-Eui Graduate School of Computer Software Engineering 2014

    49 Wernerfelt, B., "A resource-based view of the firms" 5 : 171-180, 1984

    50 Lee, O. S., "A model on priority-setting of technology investment based on demand response degree and technology gap: In the case of geo-technology" Chungbuk National University 2013

    51 Korea Federation of SMEs, "A guide of assisting information innovation clusters by industrial sectors" 2010

    52 Korea Database Agency, "2013 database white paper" 2013

    더보기

    분석정보

    View

    상세정보조회

    0

    Usage

    원문다운로드

    0

    대출신청

    0

    복사신청

    0

    EDDS신청

    0

    동일 주제 내 활용도 TOP

    더보기

    주제

    연도별 연구동향

    연도별 활용동향

    연관논문

    연구자 네트워크맵

    공동연구자 (7)

    유사연구자 (20) 활용도상위20명

    인용정보 인용지수 설명보기

    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2026 평가 재인증평가 신청대상 (재인증)
    2020-01-01 등재 등재학술지 유지 (재인증) KCI등재
    2017-01-01 등재 등재학술지 유지 (계속평가) KCI등재
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2009-03-13 학회명변경 영문명 : 미등록 -> Korean Corporation Management Association KCI등재후보
    2009-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2007-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
    더보기

    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 1.56 1.56 1.63
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    1.75 1.7 2.494 0.42
    더보기

    이 자료와 함께 이용한 RISS 자료

    나만을 위한 추천자료

    해외이동버튼